
"""simple docstring"""


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowercase				 =					{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowercase				 =					{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}






def    _snake_case					(		snake_case__ :  Optional[Any]  ):
	A     = collections.OrderedDict()
	with open(snake_case__						,   'r'						,   encoding='utf-8'  ) as reader:
		A     = reader.readlines()
	for index, token in enumerate(snake_case__  ):
		A     = token.rstrip('\n'  )
		A     = index
	return vocab







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     Tuple   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Optional[Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Tuple   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   int	,A_  :   List[str]	,A_  :   Dict="[SEP]"	,A_  :   List[str]="[SEP]"	,A_  :   Optional[int]="[SEP]"	,A_  :   str="[UNK]"	,A_  :   str="[PAD]"	,A_  :   Dict="[CLS]"	,A_  :   Optional[Any]="[MASK]"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   List[Any]	,)  -> None:
							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_	,eos_token=A_	,sep_token=A_	,unk_token=A_	,pad_token=A_	,cls_token=A_	,mask_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							try:
								import sentencepiece as spm
							except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece'   )
								raise

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							A     = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

							for i in range(10   ):
								A     = F'[unused{i}]'
								A     = 5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							A     = 12
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
								self.unique_no_split_tokens.append(A_   )


						def __getstate__(			self  :   Tuple   )  -> List[str]:
							A     = self.__dict__.copy()
							A     = None
							return state


						def __setstate__(			self  :   Dict	,A_  :   int   )  -> str:
							A     = d
							try:
								import sentencepiece as spm
							except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece'   )
								raise

							# for backward compatibility
							if not hasattr(self	,'sp_model_kwargs'   ):
								A     = {}

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

							if token_ids_a is None:
								return ([0] * len(A_   )) + [1]
							return ([0] * len(A_   )) + [1] + ([0] * len(A_   )) + [1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]

							if token_ids_a is None:
								return len(token_ids_a + sep   ) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep   ) * [0]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							return len(self.sp_model   ) + self.fairseq_offset


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Any:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   str   )  -> str:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Union[str, Any]   )  -> int:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   List[str]   )  -> str:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Union[str, Any]   )  -> Tuple:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = os.path.join(
							    A_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

							if token_ids_a is None:
								return token_ids_a + [self.sep_token_id]
							A     = [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_					=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SwinForImageClassification",
      "SwinForMaskedImageModeling",
      "SwinModel",
      "SwinPreTrainedModel",
      "SwinBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSwinForImageClassification",
      "TFSwinForMaskedImageModeling",
      "TFSwinModel",
      "TFSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swin import (
        SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinBackbone,
        SwinForImageClassification,
        SwinForMaskedImageModeling,
        SwinModel,
        SwinPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_swin import (
        TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSwinForImageClassification,
        TFSwinForMaskedImageModeling,
        TFSwinModel,
        TFSwinPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



